17 results about "Local planning" patented technology

The invention relates to a self-balancing bicycle robot multi-task path planning method, which belongs to the technical field of mobile robot autonomous navigation, and comprises the following steps: constructing an environment grid model containing static obstacle information, and fusing obstacle repulsive force and a Voronoi diagram skeleton to guide and generate an environment safety potential field; receiving a multi-task target point sequence, and calculating an optimal cut-in course angle of each target point; performing global path search in a three-dimensional state space by adopting a forward-backward bimodal primitive to generate a global path meeting incomplete kinematics constraints; the local planning adopts an improved dynamic window algorithm, dynamic stability verification and pruning are carried out in the space of the linear speed and the front wheel steering angle, and an optimal instruction is selected. The technical problems of discontinuous path, dynamic instability, poor obstacle avoidance safety and the like during multi-task operation of the self-balancing bicycle robot are solved, and the operation safety, smoothness and efficiency of the robot in a complex unstructured environment are effectively and remarkably improved.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

In conventional robot navigation techniques learning and planning algorithms act independently without guiding each other simultaneously. A method and system for robotic navigation with simultaneous local path planning and learning is disclosed. The method discloses an approach to learn and plan simultaneously by assisting each other and improve the overall system performance. The planner acts as an actuator and helps to balance exploration and exploitation in the learning algorithm. The synergy between dynamic window approach (DWA) as a planning algorithm and a disclosed Next best Q-learning (NBQ) as a learning algorithm offers an efficient local planning algorithm. Unlike the traditional Q-learning, dimension of Q-tree in the NBQ is dynamic and does not require to define a priori.

This invention discloses a collaborative path planning method based on multi-constraint improved A* algorithm and a dynamic look-ahead mechanism. It constructs a feature distance field map containing a nonlinear safety potential field, transforming discrete environmental information into continuous safety gradients. An improved A* algorithm, incorporating dynamic adaptive heuristic weights, nonlinear safety potential field costs, and kinematic smoothness constraints, is used to plan the globally optimal path. A velocity-adaptive dynamic look-ahead mechanism is established to calculate dynamic look-ahead points that conform to the current motion state in real time. A multi-objective evaluation function containing a global traction index is introduced into the local planning process to optimize control commands by measuring the deviation between the local trajectory and the global intent. This invention effectively solves the problems of "wall-hugging effect," high computational cost in large-scale environments, and poor coordination between global and local planning in traditional methods, improving the navigation safety and smoothness of robots in complex unstructured scenarios.

This invention relates to a multi-task path planning method for a self-balancing bicycle robot, belonging to the field of autonomous navigation technology for mobile robots. The method includes: constructing an environmental grid model containing static obstacle information; fusing obstacle repulsion forces and a Venn diagram skeleton to guide the generation of a safe environmental potential field; receiving a sequence of multi-task target points and calculating the optimal heading angle for each target point; performing a global path search in a three-dimensional state space using forward-backward dual-modal primitives to generate a global path that satisfies nonholonomic kinematic constraints; and employing an improved dynamic window algorithm for local planning, performing dynamic stability verification and pruning within the space of linear velocity and front wheel steering angle to select the optimal instruction. This invention solves the technical problems of discontinuous paths, dynamic instability, and poor obstacle avoidance safety in multi-task operations of self-balancing bicycle robots, effectively and significantly improving the safety, smoothness, and efficiency of robot operations in complex unstructured environments.

The application discloses a kind of intelligent vehicle local path planning method, device and storage medium, wherein method includes: according to positioning information and high-precision map information, the reference driving path required for intelligent car local path planning is obtained;According to the planning starting point of current planning frame is solved according to tracking effect;Smooth the reference driving path, obtain path frenet coordinate system according to the reference driving path after smoothing;Static obstacles and oncoming vehicles are projected to path frenet coordinate system, SL graph is generated, and path planning trajectory is obtained according to SL graph;Dynamic obstacle short-time prediction trajectory is projected to speed frenet coordinate system, ST graph is generated, and speed planning trajectory is obtained according to ST graph;Path planning trajectory and speed planning trajectory are coupled on natural coordinate system, and restore into high-dimensional local planning trajectory.The application uses SL graph and ST graph to process path and speed respectively, improves efficiency, and can be widely applied to trajectory planning technical field in unmanned driving.

This application provides a method and system for generating obstacle avoidance trajectories based on a diffusion strategy, relating to the field of intelligent control technology. The method acquires the operating status information, task target information, and environmental status information of a mobile robot; determines the local planning area requiring local obstacle avoidance planning; identifies obstacle occupancy information within the local planning area; determines the location and local deformation of facility boundaries within the local planning area; and determines the effective passage boundary. Starting from multiple initial noise sequences, it performs multi-step inverse diffusion denoising iterations under the constraints of guiding condition features to generate multiple candidate obstacle avoidance trajectories. Candidate obstacle avoidance trajectories that conflict with obstacle occupancy information, exceed the effective passage boundary, or do not meet execution constraints are eliminated, and the target obstacle avoidance trajectory is determined from the remaining candidate trajectories. This method can improve the safety and passage efficiency of local obstacle avoidance trajectories in operating environments where the boundaries of facilities on both sides of the passage may deform.

The invention belongs to the technical field of mobile robot path planning, and provides a global-local path planning method adaptive to fruit tree inter-row operation. According to the method, a dynamic weight mechanism is introduced into an A * algorithm in global path planning, and the weight of a heuristic function is adaptively adjusted according to two factors, namely the ratio of the distance from a node to a target point and the transverse spacing of fruit trees, so that the search precision is improved; a * algorithm is utilized to pre-plan and construct a real cost matrix which is used for guiding segmented optimization of the ant colony algorithm, meanwhile, an adaptive pheromone volatilization mechanism based on a solution improvement rate is adopted to improve convergence stability, and path smoothing is realized in combination with redundant point deletion. In local planning, a path deviation cost item is introduced into a dynamic window method to quantify trajectory deviation, and a dynamic weighting strategy is adopted to adaptively adjust the weight of each evaluation function according to the obstacle distance. According to the method, efficient, smooth and stable path planning can be realized in a complex orchard environment, and the method is suitable for a mobile robot for fruit tree row-to-row operation.

The invention provides a space-time tensor task priority local planning method for an unmanned surface vessel, and belongs to the technical field of autonomous unmanned system navigation and control, and the method comprises the steps: constructing a unified three-dimensional space-time tensor based on environment perception data; the three-dimensional space-time tensor performs integrated coding on the static obstacle information and the future prediction occupation information of the dynamic obstacle, and supports collision feasibility query of constant time complexity of any position and time combination; generating guidance reference information containing an expected course based on the time parameterization reference trajectory; generating a candidate control action set based on the current motion state, carrying out collision constraint verification on candidate control actions based on the three-dimensional space-time tensor, screening to obtain a safety action set meeting the requirement of no collision constraint, and taking the deviation between the minimum predicted ship course and the expected course as a unique optimization target in the safety action set. Selecting an optimal control action; and 4, generating a local planning path based on the optimal control action, and controlling the unmanned surface vessel to sail.

The invention discloses a multi-modal fusion tidal flat robot adaptive path planning method. The method comprises the following steps: (1) dynamic environment prediction; (2) multi-source data fusion; (3) path planning dynamic constraint adaptation; (4) performing global-local planning connection; according to the method, a data fusion module, an environment modeling module, a self-adaptive planning algorithm module and a path verification and adjustment module are arranged and are used for cooperative work to realize precise navigation of the tidal flat robot. The data fusion module serves as a core data support of the whole planning method and realizes multi-level fusion of ocean mode data, remote sensing inversion data and machine vision data; and the environment modeling module constructs a dynamic environment model based on the fused data, the dynamic environment model is divided into a global environment model and a local environment model, and combination of global overall planning and local fine perception is realized. Through deep fusion of an ocean mode, remote sensing inversion and machine vision, the limitation of traditional single data source path planning is broken through.

The dammed lake capacity measurement underwater robot adaptive dynamic path planning method comprises the following steps: a shore-based system processes dammed lake images collected by a satellite subscription camera, obtains initial boundary longitude and latitude information of the dammed lake water area, and performs rasterization processing on the dammed lake water area map; after the underwater robot is launched, the shore-based system sends the divided dammed lake area boundary information and the starting position information to each underwater robot through a communication module; the underwater robot executes an initial area coverage path, a sensor carried by the underwater robot acquires real-time underwater robot bottom height information, a local planner adopts an adaptive dynamic form to calculate and obtain a new local path; according to the scanning width and the path length of the local planning path, combined with the average bottom height on the underwater robot motion path; the scanned area is compared with the initial area of the dammed lake to determine whether the underwater robot completes the area coverage scanning task.

The application provides a valet parking method with local autonomous planning decision function, comprising: acquiring multi-source perception information of a current target vehicle according to at least one information acquisition device arranged around the target vehicle; obtaining complete obstacle information around the current target vehicle based on a multi-source perception data fusion algorithm; constructing a kinematic model and a single-track model of the target vehicle, and calculating an optimal driving local path of the target vehicle when safely passing a road with obstacles according to the obstacle information; and controlling the target vehicle to return to the path for continuous driving. By adopting a combination of a high-precision map global path and a local planning path, the entire process of valet parking is guaranteed to be efficient and reliable, the compatibility of the system is improved, and the application meets the advantages of having the ability of local autonomous planning in a complex scene, being adaptive to a complex scene with lack of coverage network and dim light, and handling a sudden situation of an intruding vehicle.

The present application relates to village planning space information processing technical field, disclose a kind of village planning real scene three-dimensional data acquisition method, device, equipment and storage medium, by establishing village rough three-dimensional scene and dividing planning observation unit, make data evaluation process by overall coverage determination turn to local planning expression determination, introduce planning interaction record and respectively calculate initial collection completeness, planning importance and required collection completeness, so that system can identify lane facade, courtyard edge, eaves lower interface and public space boundary and other local space area that influence planning expression effect, by calculating reinforcement requirement value and generating supplementary collection record table, so that supplementary collection process is transformed into differentiating reinforcement processing for key planning observation unit, finally by integrating initial oblique image and supplementary collection image, realize local depth estimation and real scene three-dimensional fusion, improve the three-dimensional expression quality of key area such as narrow lane, courtyard boundary and low-texture facade.

Embodiments of the present application disclose a TEB-based path planning method, device and equipment, wherein the method comprises the following steps: determining a planning starting point of a local planning path; selecting a plurality of splicing points; generating a first part of the local planning path according to the planning starting point and the plurality of splicing points; intercepting a segment of a global planning line after a corresponding point of the last splicing point as a reference planning line; generating a second part of the local planning path according to a preset trajectory constraint function and the reference planning line; and splicing the first part and the second part of the local planning path to form the local planning path. Through the present application, the problem of left and right swinging of a robot when encountering an obstacle can be solved, and the smoothness and stability of path planning are improved.

The application provides a spatiotemporal tensor task priority local planning method for unmanned surface vehicle, and belongs to the technical field of autonomous unmanned system navigation and control, and comprises the following steps: constructing a unified three-dimensional spatiotemporal tensor based on environment perception data; the three-dimensional spatiotemporal tensor integrally encodes static obstacle information and future prediction occupation information of dynamic obstacles, supports constant time complexity collision feasibility query of any position and time combination; generating guidance reference information containing expected heading based on time parameterized reference trajectory; generating a candidate control action set based on the current motion state, performing collision constraint verification on the candidate control action based on the three-dimensional spatiotemporal tensor, screening to obtain a safe action set satisfying the non-collision constraint, and selecting an optimal control action in the safe action set with the only optimization target of minimizing the deviation between the predicted ship heading and the expected heading; and step four, generating a local planning path based on the optimal control action, and controlling the unmanned surface vehicle to sail.

The invention discloses a path planning method and system based on geometric heuristic and dynamic window fusion, and belongs to the technical field of robot path planning. According to the method, an improved A * algorithm is adopted to perform global path planning, geometric constraint information of a current node and starting and ending points is introduced into a heuristic function to enhance search purposiveness, and a self-adaptive neighborhood pruning strategy is designed to reduce invalid expansion; secondly, smoothing the generated global path by adopting a B spline curve; and finally, in a local path planning stage, improving an azimuth angle evaluation function of a DWA algorithm, and generating dynamic sub-target points in real time by using the smoothed global path so as to enhance the global guidance of local planning. According to the invention, through tight fusion of global optimal path guidance and local real-time obstacle avoidance, the efficiency and smoothness of path planning and the reliability in a dynamic environment are significantly improved, and the autonomous navigation performance of the mobile robot is effectively enhanced.

The application provides a binding robot system and a path planning method and a working method thereof, the path planning method comprising: global planning: performing rapid and low-dimensional rough planning in a task space, establishing a safe buffer zone around an obstacle by introducing a concept of an extended distance; adopting a bidirectional search strategy to improve search efficiency; and generating a safe and smooth 'path main line' through a smoothing optimization strategy; local planning: performing real-time local planning under the guidance of the global 'path main line' by using a neural network algorithm based on a QR-DQN architecture to obtain an optimal binding path; and the neural network algorithm is trained by integrating target guidance and obstacle avoidance as heuristic knowledge into a reward function. Compared with the prior art, the binding robot system and the path planning method and the working method thereof provided by the application can significantly improve the adaptability, efficiency and reliability of robot binding.